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System dynamic modeling on construction waste management in Shenzhen, China Vivian WY Tam, Jingru Li and Hong Cai Waste Manag Res 2014 32: 441 originally published online 9 April 2014 DOI: 10.1177/0734242X14527636 The online version of this article can be found at: http://wmr.sagepub.com/content/32/5/441

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WMR0010.1177/0734242X14527636Waste Management & ResearchTam et al.

Original Article

System dynamic modeling on construction waste management in Shenzhen, China

Waste Management & Research 2014, Vol. 32(5) 441­–453 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0734242X14527636 wmr.sagepub.com

Vivian WY Tam1, Jingru Li2 and Hong Cai2

Abstract This article examines the complexity of construction waste management in Shenzhen, Mainland China. In-depth analysis of waste generation, transportation, recycling, landfill and illegal dumping of various inherent management phases is explored. A system dynamics modeling using Stella model is developed. Effects of landfill charges and also penalties from illegal dumping are also simulated. The results show that the implementation of comprehensive policy on both landfill charges and illegal dumping can effectively control the illegal dumping behavior, and achieve comprehensive construction waste minimization. This article provides important recommendations for effective policy implementation and explores new perspectives for Shenzhen policy makers. Keywords Construction waste, illegal dumping, landfill charges, Shenzhen, system dynamics, waste management

Introduction With the development of large-scale infrastructure and a growing demand of new houses and dismantling, construction waste generation is significantly increased. Every 10,000 m2 of construction process will produce about 500–600 tonnes of solid waste, which accounts for about 30–40% of the total waste generation (Wang and Cai, 2004). An increase in construction waste resulting in a large amount of waste generated from raw materials and energy is no doubt affecting the socio-economic and urban environment. With the recognition of sustainable development, the construction industry is aware of mitigating its adverse effects on the environment, for the importance of waste management. Research on construction waste management began in the 1970s, but it became a pressing issue only in the 1990s. It was originally focused on waste source control (Po et al., 2006). It has recently conducted diverse directions on construction waste management research, including developing technical improvements (Dainty and Brooke, 2004; Poon et al., 2001; Tam and Tam, 2006); strengthening field management and waste sorting process (Li, 2010; Poon et al., 2001; Poon et al., 2004; Shen et al., 2004; Tam, 2008); and arranging pre-contract requirements for waste management and its control (Tam et al., 2007). Relevant governmental departments have also made efforts to manage economic policies, which is recognized as landfill charging schemes for reducing construction waste generation (Chui, 2007). It has many successes in the implementation from foreign countries. Lebanon landfill charging policy (Formoso et al., 1993) and United Kingdom landfill tax and landfill tax credit policy (Morris and Read, 2001) have achieved a significant reduction in the amount of construction waste sent to landfill.

Implementing incentives also lead to improved waste behavior in Hong Kong (Chen et al., 2002; Tam and Tam, 2008). Chinese scholars have raised awareness from the successful foreign policies. The implementation of the necessary incentive measures can effectively control construction waste (Shen, 2005; Tan, 2011). The landfill charging policy itself can be very effective; however, if incorrectly implemented the policy can cause serious problems on illegal dumping. Government is necessary to understand the possible outcomes, rather than pursuing a blind implementation. Therefore, it is necessary to simulate appropriate methods for policy implementation for construction waste management. System dynamics was developed by Professor Jay W Forrester in 1958 using a feedback control theory and computer simulation technology as an adjunct for providing quantitative analysis on complex systems (Zhou et al., 2005). The method for the study on complex systems, starting from the system as a whole, focused on analysis within the system causal association among variables, and information feedback loop dynamic behavior (Gu and Cai, 2007; Yan et al., 2002).

1School

of Computing, Engineering and Mathematics, University of Western Sydney, Penrith, NSW, Australia 2College of Civil Engineering, Shenzhen University, Shenzhen, China Corresponding author: Jingru Li, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China. Email: [email protected]

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System dynamics have been widely applied to complex social, economic and ecosystem areas (Cheng et al., 2004). Practical study of complex feedback systems have gradually been introduced for waste management systems (Dyson and Chang, 2004). A system dynamic model was developed to predict required landfill areas and process capacity of solid waste management (Mashayekhi, 1993). With the use of fuzzy logic and system dynamics modeling, qualitative variables for processing construction waste were developed (Karavezyris et al., 2002). Waste generation and management of the complex processes were linked to the research involved in cost and benefit of economic interaction (Naushad et al., 2010). Economic effectiveness of construction waste management using a system dynamic approach was conducted (Yuan and Wang, 2013). It was proved that the model established is reliable and effective for assessment. A model that can serve as a decision support tool for projecting construction and demolition waste reduction, in line with the waste management situation of a given project, was also developed (Yuan et al., 2012). A dynamic decision model was developed to achieve dynamic optimization of materials collection and to facilitate recycling management (Anghinolfi et al., 2013). A theoretical model to assess construction projects in terms of their sustainable development value and sustainable development ability for the implementation of the project life cycle was developed (Zhang et al., 2014). The use of system dynamics for solid waste management has been preliminary studied in Mainland China. A system dynamics model on analyzing municipal solid waste problems was established for exploring the Beijing population, economy and dynamic development on waste management for decisionmaking by the government (Lin, 2006). A simulation model for strategic planning of construction waste management, and to help decision-makers and parties involved to better grasp and understand the architecture involved in waste management in complex information and operational processes in Hong Kong, was established (Hao et al., 2007). A construction waste management model has also been established to simulate the costbenefit of construction waste management practices over the project duration (Yuan et al., 2011). System dynamics has proved to be an effective tool for simulating the effects on policy implementation. This article develops a system dynamic model for assessing the effectiveness of construction waste management in Shenzhen, Mainland China. Shenzhen is selected in this article as it is the first pilot city for comprehensive construction waste management in China. It has also been conducting construction waste minimization and recycling for several years and thus possesses certain useful data for our study. It should be emphasized that the system dynamic method used in this study is also applicable to other cities. The model has used economic means as the main control indicators for policy simulation. Analysis of construction waste from “source product” to “end process” among main variables is also explored. The developed model can help to solve problems on

lack of historical data on construction waste and can be used for further studies. The process of feedback associated with feasible solutions, suggested in this article for developing appropriate management policies, can provide valuable references to the government in policy development and implementation.

A construction waste management system dynamics model Description of the model This article develops a system dynamics model for construction waste management in Shenzhen, Mainland China. The developed system dynamic model can quantitatively reflect construction waste management processes involving feedback between each influencing factor and its interrelation. This system dynamics has set a time boundary as 2011–2030, with 2011 as the base year and main historical data from the period of 1990–2010. To reduce the prediction error caused by changes in time period, the time step is set as 1 year. Research object boundaries are set as construction waste generated by new buildings, housing decoration, road reconstruction and old building demolition (including demolition of old buildings, reconstruction of village, transformation of old industrial building and large municipal construction projects). The overall construction waste management system is divided into several subsystems. Research on the various subsystems and inherent linkages among variables is conducted. Five subsystems are used in this article: (i) a construction waste generation subsystem; (ii) a transportation subsystem; (iii) a landfill subsystem; (iv) a resource recovery subsystem; and (v) an illegal dumping subsystem. The various subsystems can influence each other, constituting a causal association with multiple feedback.

A causal loop diagram Figure 1 reveals an architectural model of a causal loop diagram for construction waste management. Causal loop diagrams can clearly reflect positive and negative correlations between variables, mainly composed of multiple positive and negative feedback loops within the boundaries of the system. As can be seen from Figure 1, the construction waste management system consists of several common encirclements. The management objectives are to protect the environment and save land resources, which are most concerned about by local government. The increasing generation and disposal of construction waste (including waste disposed at landfills and illegally dumped waste) will surely occupy large amounts of land areas. The government must take measures to reverse the malignant trend. The government may request to strengthen on-site management and reduce waste generation, call for waste classification to reduce the ratio of the waste that needs to go to landfills, establish and improve the recycling market for additional waste recycling and reduce illegal dumping by increasing the punishment of illegal

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Tam et al. Amount of waste generation

-

+ Using recycled materials + Amount of waste transportation

+

+ Amount of waste unable to dispose -

On-site waste sorting +

Landfill charges

+

+

+

+ Amount of landfill Illegal waste dumping

+

+

Recycled material accumulation

+ Landfill capacity usage + - Disposing capacity of recycling companies

Waste management +

+Cost for waste transportation +

+

Market sophistication +

+

+

+

Occupied land areas

Construction progress for new landfills

Fine efforts on illegal dumping +

Waste sorting levels

+

+

+

Management efforts

Figure 1.  A causal loop diagram of the construction waste management system.

dumping so as to reduce occupied land areas. Besides that, the government can charge for waste to be landfilled, which will promote the construction enterprises’ on-site waste classification and improve the utilization ratio of the waste. However, increasing landfill charge may cause additional illegal dumping. It calls for stronger control of government by means of increasing the punishment for illegal activities. Furthermore, recycling construction waste and construction of new landfills will reduce landfill pressure.

System flow diagram Figure 2 shows a system flow diagram with all subsystems on construction waste management developed in the system dynamic model. This model system consists of five subsystems. 1. Generation subsystem and transportation subsystem. Construction waste includes building construction waste, building decoration waste, road reconstruction waste, old industrial building transformation waste, old village reconstruction waste and municipal engineering demolition waste. Among them, building construction waste can be sorted onsite, in which metal and timber can be reused or recycled. The rest of the building construction waste is partly used to subgrade landfills, and partial pure inert waste is transported to waste recycling companies. The other mixed waste is transported to landfills. The majority of road reconstruction waste is inert waste, and delivered to recycling companies. Building decoration waste is transported to the landfills directly.

With regard to old industrial building transformation waste, old village reconstruction waste and municipal engineering demolition waste, waste steel is firstly sorted and sold out. The sorted inert waste is transported to the recycling company and the other mixed waste is transported to the landfills. Of course, the level of unit landfill charge and the government management effort will have a great influence on the on-site sorting rates and the backfill rate. 2. Recycling subsystem. Recycling companies receive the sorted inert waste as major raw materials to produce recycled building materials. Stacking waste materials occupies land areas. The amount of waste recycling each year is limited by the recycling capacity of the plants. And the amount of inert waste to recycling companies is influenced by the transportation distance to recycling companies, specific landfill charges and markets. With the increasing of the number of recycling companies, transportation distance to recycling companies will decrease, and then reduce the unit transportation cost for recycling. However, construction of new recycling plants will occupy additional land area. 3. Illegal dumping subsystem. During transportation, part of construction waste can be illegally dumped, which will occupy a great amount of land areas. The additional cost difference is between illegal dumping and landfills, and the high illegal dumping percentage. The increment of illegal dumping waste will lead the government to devote additional management effort. The stringent policy implementation will raise fine probability and thus decrease the cost difference (interest driven). It will therefore reduce the illegal dumping percentage.

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Figure 2.  System flow diagram. (a) Waste generation subsystem and transportation subsystem; (b) recycling subsystem and landfill subsystem; (c) illegal dumping subsystem.

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Tam et al. 4. Landfill subsystem. Except the construction waste transported to recycling companies and illegally dumped, the other part is delivered to landfills. The landfill waste occupies landfill areas and increases landfill usage proportion. The high landfill usage proportion will increase government management effort on reducing waste generation and illegally dumped waste, and increase the waste for recycling. Government will also build new landfills in accordance with city planning. Then the landfill capacity will increase, which can reduce the average transportation distance to landfills. The average transportation distance to landfills will be affected by the rate to recycling companies and illegal dumping percentages. This model consists of 103 variables, including seven stock variables, seven flow variables and 89 convertor variables. Major variables quoted in the model are tabulated explicitly in Table 1.

Model simulation Data collection and quantification This article is conducted based in Shenzhen, Mainland China. Shenzhen, as one of the first Chinese cities implementing comprehensive construction waste management, has made some achievement. Three construction waste recycling plants have been established since 2008. A local legislation on the comprehensive utilization of construction waste was enacted in 2009. As mentioned above, Shenzhen is also the first Chinese pilot city for comprehensive construction waste management. Thus, the data used in our study can be relatively easily obtained in this city. The data source is based on the 1990–2010 Statistical Yearbook in Shenzhen, governmental documents, authoritative websites and previous literatures (Kang, 2005; Lin, 2010). Data using statistical regression techniques was obtained. Ten randomly selected construction projects were also studied during February to March 2012 with semi-structured interviews of their project professionals to further investigate waste management practices in Shenzhen, Mainland China. The flow and stock variables are respectively calculated by means of the flow equation and the stock equation. Convertor variables consist of three categories: W1, W2 and W3. W1: convertor variables are constant, and their value can be obtained from the government documents, statistics, literatures and interview. For example, according to “Construction Waste Management Specifications in Shenzhen”, the waste generation indicator of new building construction is identified as 36 kilogram m-2, incorporating about 12% scrap metal and 19.5% scrap timber. The value of mixed construction waste density, 1.6 tonne m-3, is an experience value provided by frontline workers. W2: the values of convertor variables are determined by other variables based on mathematical equations. For example, it is observed from “The statistical yearbook of Shenzhen in 2011” that the new construction building area (i.e. FSOBUC in Figure 3)

is related with the local GDP. Thus, the regression relations are analyzed using SPSS13.0 based on GDP and the new building construction area of Shenzhen from 1990 to 2010. The analysis results are shown in Figure 3. According to Figure 3, the coefficient of determination R2 is 0.869, which indicates that the equation fits data well. A certain logarithmic function exists between GDP and new building construction area, whose expression is: New building construction area = 1079.736 * ln ( GDP ) − 4487.915 W3: Convertor variables change with time or other variables, but there are no mathematical equations suitable to describe their relationship. The table function in Stella is used to express the relationship between convertor variables and other variables. As an example, Figure 4 shows the relationship between variables of specific landfill charges and sorting rate 1. The on-site sorting rate of new building construction waste is 15% (only metal is sorted) when the landfill charge is on the current level (5 yuan or US$0.82 tonne-1). While the on-site sorting rate can reach 80% (inert concrete and brick are also sorted out) when the landfill charge becomes 160 yuan or US$26.40 tonne-1.

Model testing Model testing, including mechanical error checking, extreme conditions checking and sensitivity analysis, is conducted. Mechanical error checking is testing simple non-systematic errors and/or non-logical errors. This carefully verifies the mathematical equations developed in the model. Using an in-built verification feature in the Stella software, this can ensure both ends of the equations with consistency dimension and arithmetic symbols are correct. Extreme condition checking is testing the model equations with/without meaningful results even under extreme conditions, especially for the flow equations. It is found that seven flows are still valid under extreme conditions. For example, when gross domestic product (GDP) growth rate is set as zero, the GDPs of each year are kept the same; when flow variables such as waste generation is zero, the corresponding stock variables is zero. These are in line with the actual situation and are meaningful. Sensitivity analysis is testing of any parameter with regard to significant impacts on the model simulation result after varying the parameter values. If it is significantly changed, the parameters are highly sensitive parameters. Stella simulation software provides a sensitivity analysis by running the menu Sensi Specs option. Sensitivity analysis for model validation has been successfully used by Yuan et al. (2011). Our study also used sensitivity analysis for model validation. As shown in Figure 5, when the landfill charge increases, the waste transported to the recycling plants will increase too. The results are in accordance with the real-world situation in Shenzhen, as mentioned by Kang (2005). The sensitivity analysis results indicate that the model is valid.

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Table 1.  Descriptions of variables quoted in the model (main variables). Variables

Unit

Variable type

Quantification method

Descriptions  

Waste generation subsystem Increasing GDP GDP Waste generating

108 yuan 108 yuan Tonnes year-1

Flow Stock Flow

Total waste generated

104 tonnes

Stock

GDP increasing rate Building construction indicator Building construction area Building construction waste Timber waste ratio

% 104 tonnes m-2

Convertor Convertor

W3 W1

104 m2

Convertor

W2

104 tonnes

Convertor

W2

%

Convertor

W1

Metal waste ratio

%

Convertor

W1

Reusing and recycling rate of metal Reusing and recycling rate of timber Backfill rate Subgrade landfill Sorting rate 1

%

Convertor

W3

The growth value of GDP The total GDP of current year The quantity of construction waste produced in current year The cumulative generation amount of construction waste The annual growth rate of GDP The quantity of construction waste per unit construction area New building construction area per year The amount of waste generated in new building construction The proportion of timber in building construction waste The proportion of metal in building construction waste The ratio of metal reclamation

%

Convertor

W3

The ratio of timber reclamation

% 104 tonnes %

Convertor Convertor Convertor

W3 W2 W3

Sorting inert waste 1

104 tonnes

Convertor

W2

Mixed waste 1

104 tonnes

Convertor

W2

Building decoration waste Road reconstruction waste Demolition waste

104 tonnes

Convertor

W2

104 tonnes

Convertor

W2

104 tonnes

Convertor

W2

Old village indicator

tonne m-2

Convertor

W1

Old village reconstruction area Old village reconstruction waste Old industrial building indicator Old industrial building transformation area Old industrial building transformation waste Municipal engineering indicator Municipal engineering demolition area Municipal engineering demolition waste Sorting rate 2

104 m2

Convertor

W2

104 tonnes

Convertor

W2

tonne m-2

Convertor

W1

104 m2

Convertor

W2

104 tonne

Convertor

W2

tonne m-2

Convertor

W1

104 m2

Convertor

W2

104 tonnes

Convertor

W2

%

Convertor

W3

Sorting inert waste 2

104 tonnes

Convertor

W2

Mixed waste 2

104 tonnes

Convertor

W2

The proportion of backfill on-site The quantity of backfilled waste The sorting ratio of new building waste Inert waste sorted out from new building waste The mixed waste left after sorting the new building waste The waste generated in building decoration The waste generated in road rebuilding The total amount of demolition waste The waste generated index of old village renovation The reconstruction area of old village The waste generated in old villages reconstruction The waste generated index of old industry building transformation The transformation area of old industrial building The waste generated in transforming old industrial building The waste generated index in municipal engineering renovation The renovation area of municipal engineering The waste generated in renovating municipal engineering The sorting ratio of demolition waste on-site Inert waste sorted out from demolition waste The mixed waste left after sorting demolition waste

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Tam et al. Table 1. (Continued) Variables

Unit

Variable type

Transportation subsystem Removal waste

104 m3

Flow

Total removal waste

104 m3

Stock

Removing inert waste Removing mixed waste Landfill subsystem Landfill waste Total landfill waste

104 m3 104 m3

Convertor Convertor

104 m3 104 m3

Flow Stock

Original landfill capacity Landfill capacity Landfill usage proportion Remaining landfill capacity Transportation distance to landfill Landfill occupy land Total land area

104 m3 104 m3 %

Convertor Convertor Convertor

W1 W2 W2

104 m3

Convertor

W2

km

Convertor

W3

104 m2 104 m2

Convertor Convertor

W2 W2

Government management effort Illegal dumping subsystem Illegal dumping waste Total illegal dumping waste Illegal dumping percentage Fine amount

1

Convertor

W3

104 m3 104 m3

Flow Stock

%

Convertor

W3

Yuan 10 tonnes-1 %

Convertor

W1

Convertor

W3

Yuan tonne-1

Convertor

W2

Convertor

W1

Transportation distance for illegal dumping Unit landfill charge Unit saving cost

Yuan tonne-1 km-1 km

Convertor

W1

Yuan tonne-1 Yuan tonne-1

Convertor Convertor

W1 W2

Interest driven

Yuan tonne-1

Convertor

W2

Illegal dumping occupy land Recycling subsystem Raw material Raw material accumulation

104 m2

Convertor

W2

104 m3 104 m3

Flow Stock

Waste recycling Total recycled waste

104 m3 104 m3

Flow Stock

Fine probability Unit cost for illegal dumping Unit transportation cost

Quantification method

W2 W2

Descriptions   The waste transported out from construction site The cumulative amount of the construction waste transported out The inert waste transported The mixed waste transported   The waste transported to landfill The cumulative amount of waste transported to landfill Initial landfill capacity Current landfill capacity The occupational ratio of landfill capacity The remaining storage capacity of landfill The transported distance to the landfill The area of landfill occupancy The total area of landfill, recycling company and illegal dumping occupancy The government effort on construction waste management   The waste illegally dumped The cumulative amount of waste illegally dumped The proportion of illegally dumped waste in the waste transported Fines for illegal dumping The probability of being fined for illegal dumping The expectation value of fine The unit transportation cost of waste The average transportation distance of illegal dumping The unit landfill charge of waste Compared with the delivery of landfill the cost decreased by illegal dumping The difference between unit saving cost and unit cost for illegal dumping Land occupation of illegal dumping waste   Raw materials of recycled materials The cumulative value of raw material of recycling building materials Recycled raw material Accumulated recycled raw material (Continued)

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Table 1. (Continued) Variables

Unit

Variable type

Quantification method

Descriptions

Waste to recycling company Rate to recycling company

104 m3

Convertor

W2

%

Convertor

W2

Market sophistication

1

Convertor

W3

Unit cost for recycling

Yuan tonne-1

Convertor

W2

Unit cost for landfill

Yuan tonne

Convertor

W2

Cost difference

Yuan tonne-1

Convertor

W2

Transportation distance to recycling company Recycling capacity

km

Convertor

W3

104 m3

Convertor

W2

Capacity increasing

104 m3

Convertor

W1

Original capacity Recycling factory occupy land Sorting rate 3

104 m3 104 m2

Convertor Convertor

W1 W2

%

Convertor

W3

The waste transported to recycling company The proportion of waste transported to recycling company and the inert waste sorted out The influence degree of recycled products market maturity The transportation cost per tonne of waste to recycling company The transportation cost per tonne of waste to landfill The difference between transportation cost to recycling company and to landfill The transportation distance to recycling company The current capacity of recycling company Grown capacity of recycling company Initial capacity of recycling company The occupational area of recycling company The regeneration rate of waste being recycled for raw materials

GDP: gross domestic product.

Figure 3.  Regression analyses of GDP and the new building construction area. FSOBUC: Floor Space Of Buildings Under Construction; GDP: gross domestic product.

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Model simulation results and analysis A construction waste management system is a huge complex system, involving several stakeholders, such as contractors, transportation companies and recycling companies. Implementation of construction waste management also requires a lot of qualitative policies. This article quantitatively studies economic policies by observing and evaluating its impacts on the construction waste management system. It helps to optimize decision-making objectives and to achieve effective waste management. Table 2

Figure 4.  Relationship between variables of unit landfill charge and sorting rate 1 (data source: Kang, 2005) (104 m3).

summarizes three policy options, which will be simulated using the developed model. Policy Option I: No control program.  No control program is first used to simulate the construction waste management system under existing policy environment. This program can simulate the natural development trend on construction waste management in Shenzhen. Figure 6 is simulating total construction waste generated (Line 1), total removal waste (Line 2), total landfill waste (Line 3), total recycled waste (Line 4) and total illegal dumping waste (Line 5) within existing policy environment. It is predicting that total amount of waste generated (excluding excavated soil) will reach about 138,499,100 cubic meters at density of 1.6 tonnes per cubic meter, which is the equivalent to about 222 million tonnes. A total of about 81,720,900 cubic meters (about 131 million tonnes) of construction waste will be directly sent to landfills, accounting for about 59% of the total generation. The development trends for the total construction waste generation (Line 1) and total removal waste (Line 2) are similar from the initial until a clear separation between the two lines at the later forecast period. This indicates that both construction waste generation and removal waste are increasing and receive about 13.41% of the site recovery rate until the end of the forecast period. A slow development trend on illegally dumping waste of about 2,130,700 cubic meters (approximately 3,409,000 tonnes) is also forecasted in the simulation. The growth rate becomes

Figure 5.  An example of model testing (quantity of total recycled waste). (1) for unit landfill charge = 5; (2) for unit landfill charge = 43.8; (3) for unit landfill charge = 82.5; (4) for unit landfill charge = 121; (5) for unit landfill charge = 160. Table 2.  Three policy options and control variables. Policy options I II III

Control variables No control program Landfill charging policy Comprehensive economic policy

No Unit landfill charges Unit landfill charges and fines for illegal dumping

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Figure 6.  Simulation on Policy Option I.

(1) Total waste generated; (2) total removal waste; (3) total landfill waste; (4) total recycled waste; (5) total illegal dumping waste.

Table 3.  Simulation results of landfill charging policy. Unit landfill charges (yuan tonne-1)

Policy Option I

Policy Option II*



5

20

40

60

80

100

120

140

Unit landfill charges (US$ tonne-1) Total waste generated Total removal waste Total recycled waste Total illegal dumping waste Total landfill waste Landfill usage proportion

0.83

3.33

6.67

10

13.33

16.67

20

23.33

138.50 106 m3 119.92 106 m3 37.59 106 m3 2.13 106 m3 81.72 106 m3 70%

0 0 +8.0% +26.8% –4.2% –4.2%

0 0 +26.0% +68.1% –13.2% –13.2%

+0.18% +0.15% +48.4% +115.2% –24.1% –24.1%

+0.29% +0.24% +69.8% +155.4% –34.4% –34.4%

+0.39% +0.32% +88.6% +180.2% –43.1% –43.1%

+0.41% +0.34% +100.8% +194.7% –48.8% –48.8%

+0.43% +0.36% +110.4% +197.6% –53.0% –53.0%

*: the data in policy option II is compared with Policy Option I.

slow in the later stage because the government management effort becomes more rigorous. It should be noted that Line 4 refers to total recycled waste with a gradual improvement. Based on the increase on recycling capacity, the total recycled inert waste is forecasted as 37,587,200 cubic meters, about 27.15% of total waste generated. This rate may still sounds low compared with developed countries. Additionally, this article assumes that landfills have an annual increment of about an additional 4,785,000 cubic meters. It is predicted that the landfills will reach about 70% of the total usage at the end of the forecast period. Relevant pre-planning of at least 20% is required to reserve landfill space for unexpected urban construction activities, this policy is tight for the current development. Based on the simulation results, Shenzhen will face a huge environmental and land pressures if policy is not effectively implemented. The Shenzhen municipal waste management model simulates that the situation will be increasingly serious, which requires planning for new landfills over the next few years to deal with the accumulation of a large number of construction waste (Hao et al., 2010).

Policy Option II: Landfill charging policy.  Landfill charging is an important economic leverage, directly involving all main parties involved in economic interests. The landfill charging fee must be determining based on local conditions and balancing interests of the parties involved. Consideration should also be made clearly into psychological and triggering acts of illegal dumping. Shenzhen landfill charges are currently levied at only 5 yuan tonne-1 (or US$0.83 tonne-1), but fortunately this model can provide parameter control through comparison on decisionmaking optimization. With the changes of the landfill charging parameters to 20–140 yuan tonne-1 (or US$3.33–23.33 tonne-1) with a 20 yuan tonne-1 (or US$3.33 tonne-1) incremental increase, the simulation results are shown in Table 3. Based on the simulation results, both construction waste generated and the removal waste are not sensitive to implementation of landfill charging policies. Source reduction of construction waste is more dependent on other factors, such as technology and skill. Although the landfill charging fee is not sensitive to total construction waste generated, it still receives reasonable improvement of the landfills and recycling. The waste disposed in landfill obviously decreased by 53%. At the same time, the

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Figure 7.  Intensity of illegal dumping fines with relation to the illegal dumping of waste.

Note: Lines 1–4 are fined at 2000, 4000, 6000 and 8000 yuan per vehicle (or US$333.33, 666.67, 1000 and 1333.33), respectively.

Table 4.  Integrated intensity of landfill charges and fine policy control solutions. Policy

Landfill charges (yuan tonne–1)

Landfill charges (US$ tonne-1)

Fine efforts (yuan vehicle-1)

Fine efforts (US$ vehicle-1)

Scheme I Scheme II Scheme III

5 80 100

0.83 13.33 16.67

2000 9400 11,430

333.33 1566.67 1905

recycled waste increased by 110.4%. This indicates that the implementation of the landfill charging policy has a positive impact on construction waste disposal. The landfill capacities are also reduced from 70% to 31% in the simulation. It should be noted that the decline in landfill capacity utilization means that an increased amount of landfill capacity will be an idle waste of resources. Therefore, the government can slow down the progress of the landfill construction with the implementation of the landfill charging policy. It may be necessary to transfer the resources to illegal dumping instead. It predicts an increase of about three times the amount on illegal dumping, about 6.342 million cubic meters, could result when the landfill charging fee reaches 140 yuan tonne-1 (or US$23.33 tonne-1) compared with 5 yuan tonne-1 (or US$0.83 tonne-1). This indicates that the higher the fee, the more illegal dumping. Therefore, if increasing the landfill charging fee, it should also increase the illegal dumping fine to avoid the consequence. It should be noted that the impact of the landfill charging fee tends to recede with a limited increase on recycled waste and decrease on landfill waste when it increases to at least 100 yuan tonne-1 (or US$16.67 tonne-1) . Policy Option III: Comprehensive economic policy.  It is clear that the development of rational landfill charges can achieve a certain degree of landfill waste reduction and increase waste recovery, but it also leads to additional frequent illegal dumping

behavior. Thus, in the third option, increasing the amount of the fine with the augment of landfill charges is also taken into consideration to control illegal waste dumping. It is illustrated by Figure 7 that the amount of illegal dumping will reduce when the fines are boosted for illegal dumping. From the previous analysis, optimal landfill charges are identified as 80–100 yuan tonne-1 (or US$13.33–16.67 tonne-1). Thus, this article selects landfill charges on 80 yuan tonne-1 (or US$13.33 tonne-1) and 100 yuan tonne-1 (or US$16.67 tonne-1) for two simulation schemes of comprehensive policy. Once the landfill charge is determined, the value of fine increases gradually until the illegal dumping waste debase to no more than that in Policy Option I (2,131,000 cubic meters). After simulation, it is found that the fine needs to be raised to 9400 yuan per vehicle (or US$1,566.67 per vehicle) if the landfill charge is set as 80 yuan tonne-1 (or US$13.33 tonne-1). When the value of landfill charges reaches about 100 yuan tonne-1 (or US$16.67 tonne-1), the ancillary penalty amount is about 11,430 yuan per vehicle (or US$1905 per vehicle). With no regulatory policy options as basic Scheme I, two additional landfill charge options, with their corresponding penalties, are referred to as Schemes II and III. All schemes are summarized in Table 4. Policy options with their simulation results are summarized in Table 5. It is clear that the implementation of landfill charging policies can maintain the amount of illegal dumping, supported with the fines increasing to a reasonable level.

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Table 5.  Comprehensive evaluation and analysis of the results of policy control. Parameters

Scheme I

  138.50 106 m3 119.92 106 m3 37.59 106 m3 2.13 106 m3 81.72 106 m3 70%

Total waste generated Total removal waste Total recycled waste Total illegal dumping waste Total landfill waste Landfill usage proportion

Conclusion This article developed system dynamic models for construction waste management in Shenzhen, Mainland China using Stella simulation software. Five subsystems were included in the model: (i) construction waste generation subsystem; (ii) transportation subsystem; (iii) landfill subsystem; (iv) resource recovery subsystem; and (v) illegal dumping subsystem. This article simulated different policy options and its levels for effective construction waste management. In the current policy environment (Policy Option I), the generation of construction waste will reach 138,499,100 cubic meters. Among them, 81,720,900 cubic meters is disposed in landfill, 37,587,200 cubic meters is recycled and 2,130,700 cubic meters is dumped illegally. In Policy Option II, the landfill charge increases, construction waste generated does not change significantly, but the waste recycled increases and waste disposed in landfill reduces obviously. At the same time, the amount of illegally dumped waste can substantially increase. Therefore, in Policy Option III, the fine for illegal dumping also increases in relation to the landfill charge. The simulation results revealed that fine needs to increase from 2000 to 9400 yuan per vehicle (or US$333.33 to 1566.67 per vehicle) when the charge is 80 yuan tonne-1 (or US$13.33 tonne-1), and 11,430 yuan per vehicle (or US$1905 per vehicle) when the charge is 100 yuan tonne-1 (or US$16.67 tonne-1). This article provides important recommendations for effective policy implementation and explores new perspectives for Shenzhen policy makers.

Acknowledgements The authors are very grateful for the constructive comments provided by the two anonymous reviewers.

Declaration of conflicting interests The authors declare that there is no conflict of interest.

Funding The present study is funded by the Ministry of Education of China (11YJAZH047) and National Nature Science Foundation of China (71203065).

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Compared with Scheme I Scheme II

Scheme III

+0.45% +0.38% +65.6% 0% –30.5% –31.4%

+0.46% +0.38% +82.9% 0% –38.6% –38.6%

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System dynamic modeling on construction waste management in Shenzhen, China.

This article examines the complexity of construction waste management in Shenzhen, Mainland China. In-depth analysis of waste generation, transportati...
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